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new material

Application of Machine Learning & Deep Learning


This article was originally posted on our company website. Flexday Solutions LLC is a team of thought leaders in the fields of AI, ML and cloud solutions. In recent times, the application of ML and DL techniques in the various fields of science has enabled scientists to uncover interesting and useful insights. Specifically, in the field of materials science, scientists are constantly putting effort to design new materials for various end-use applications. There are enormous amounts of data related to different variety of materials available in the public domain.

Computational modeling guides development of new materials


Metal-organic frameworks, a class of materials with porous molecular structures, have a variety of possible applications, such as capturing harmful gases and catalyzing chemical reactions. Made of metal atoms linked by organic molecules, they can be configured in hundreds of thousands of different ways. To help researchers sift through all of the possible metal-organic framework (MOF) structures and help identify the ones that would be most practical for a particular application, a team of MIT computational chemists has developed a model that can analyze the features of a MOF structure and predict if it will be stable enough to be useful. The researchers hope that these computational predictions will help cut the development time of new MOFs. "This will allow researchers to test the promise of specific materials before they go through the trouble of synthesizing them," says Heather Kulik, an associate professor of chemical engineering at MIT.

Science and innovation relies on successful collaboration


It may sound obvious, perhaps even clichéd, but this mantra is something that must be remembered in ongoing political negotiations over Horizon Europe, which could see Switzerland and the UK excluded from EU research projects. We need more, not fewer, researchers collaborating to solve today's and tomorrow's challenges. By closely working with Swiss and British researchers, who have long played key roles, Horizon Europe projects will benefit – as they have in the past. This is the motivation behind ETH Zurich, which collaborates with IBM Research on nanotechnology, leading the Stick to Science campaign. This calls on all three parties – Switzerland, the UK and the EU – to try and solve the current stalemate and put Swiss and British association agreements in place.

How AI is becoming a research companion to materials scientists


By automating scientific processes and introducing artificial intelligence for decision-making, TRI's new closed-loop research platforms free up scientists' time for more creative tasks. When I first started graduate school almost 10 years ago, I was mixing ingredients by hand, writing down reaction conditions on a piece of paper, and grabbing a quick lunch in between lab sessions. At that time, the idea of a robot doing my experiments -- or using machine learning to predict the outcomes of my reactions -- would have never occurred to me. I accepted a future as a scientist where I would only be able to explore a tiny fraction of the billions of possible materials in the universe by hand. If lucky, a scientific discovery might arrive serendipitously as I became better at making "educated guesses."

Using deep learning to develop new materials


To develop faster computers, batteries with higher capacities, and lighter and stronger automobiles and airplanes, scientists are working to discover and synthesize new materials with high-performance properties. But the number of atom combinations that could compose new materials is nearly unlimited, which makes the discovery process time consuming and expensive. To speed up the process, scientists are using theoretical models and computers to explore all the possibilities and disregard materials that are not desirable. Ali Davariashtiyani, a PhD student working under the direction of Assistant Professor Sara Kadkhodaei in the Computational Materials Research Lab at UIC, has developed a data-driven deep-learning model to help researchers identify easily synthesizable materials. Their findings were recently published in the journal Communications Materials.

Hard machine learning can predict hard materials


Superhard materials are in high demand by industry, for use in applications ranging from energy production to aerospace, but finding suitable new materials has largely been a matter of trial and error, based on classical hard materials such as diamonds. In a paper in Advanced Materials, researchers from the University of Houston (UH) and Manhattan College report a machine-learning model that can accurately predict the hardness of new materials, allowing scientists to more readily find compounds suitable for use in a variety of applications. Materials that are superhard – defined as those with a hardness value exceeding 40 gigapascals on the Vickers scale, meaning it would take more than 40 gigapascals of pressure to leave an indentation on the material's surface – are rare. "That makes identifying new materials challenging," said Jakoah Brgoch, associate professor of chemistry at UH and corresponding author of the paper. "That is why materials like synthetic diamond are still used even though they are challenging and expensive to make."

ICYMI: We check out Android 12's visual refresh


This week, in addition to covering all the Cyber Week deals we could find, we also reviewed some unique gadgets. Steve Dent and a licensed drone pilot toured the French countryside with the help of the DJI Mavic 3 drone, while Terrence O'Brien played with the Animoog Z app, a sequel ten years in the making. Also, Cherlynn Low played around with Android 12 to check out its new Material You design. Steve Dent spent some time with the DJI Mavic 3 and a licensed drone pilot in the French countryside to see what the new device is capable of. He reports that not only is the Mavic 3 the easiest DJI drone to fly, but the large 4/3 sensor and dual camera system produce incredible footage – and the 46 minutes of range is double the time that the previous model could capture.

Materials: 'Super jelly' made from 80 per cent water can survive being run over by a CAR

Daily Mail - Science & tech

No, it's'super jelly' -- a bizarre new material that can survive being run over by a car even though it's composed of 80 per cent water. The'glass-like hydrogel' may look and feel like a squishy jelly, but when compressed it acts like shatterproof glass, its University of Cambridge developers said. It is formed using a network of polymers held together by a series of reversible chemical interactions that can be tailored to control the gel's mechanical properties. This is the first time that a soft material has been produced that is capable of such significant resistance to compressive forces. Super jelly could find various applications, the team added, from use for building soft robotics and bioelectronics through to replacement for damaged cartilage.

AI Generates Hypotheses Human Scientists Have Not Thought Of


Electric vehicles have the potential to substantially reduce carbon emissions, but car companies are running out of materials to make batteries. One crucial component, nickel, is projected to cause supply shortages as early as the end of this year. Scientists recently discovered four new materials that could potentially help--and what may be even more intriguing is how they found these materials: the researchers relied on artificial intelligence to pick out useful chemicals test from a list of more than 300 options. And they are not the only humans turning to A.I. for scientific inspiration. Creating hypotheses has long been a purely human domain.

Machine-learning system accelerates discovery of new materials for 3D printing


The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses. To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste. The machine learning algorithm could also spur innovation by suggesting unique chemical formulations that human intuition might miss. "Materials development is still very much a manual process. A chemist goes into a lab, mixes ingredients by hand, makes samples, tests them, and comes to a final formulation. But rather than having a chemist who can only do a couple of iterations over a span of days, our system can do hundreds of iterations over the same time span," says Mike Foshey, a mechanical engineer and project manager in the Computational Design and Fabrication Group (CDFG) of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-lead author of the paper.